Storage-based Smart Grid Communication Framework

In this paper we theoretically investigate the optimization problem of leveling load fluctuations on the electrical grid by means of widely distributed energy storage, and pervasive communication between grid and consumers. Allowing real-time exchange of load and generation forecasts along with storage capacity, the optimization simultaneously minimizes electrical fluctuations on the grid as well as energy costs. The optimization problem lies within the constraints of minimum, forecasted, and learned energy requirements for consumers, thermal and renewable generation capacity, weather forecasts, and storage levels, as well as within physical and stochastic models to provide accurate forecasting of energy demand on the grid. Distinct hierarchical structures of these constrains based on increased penetration of renewable generation are necessarily considered. The optimization problem, and its solutions, become the basis for a pervasive communication framework that lead to a "just-in-time" model of energy distribution, while utilizing energy generators at their maximum efficiency. This paper shows that such a conceptual framework leads to a truly "smart" grid, and is relatively generic and easy to adapt to distinct grid structures and generation sources.